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"""Cartoonset-10k Data Set"""
import pickle
import numpy as np
import PIL.Image
import datasets
from datasets.tasks import ImageClassification
_CITATION = """\
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
"""
_DESCRIPTION = """\
The Cartoonset-10k dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
per class. There are 50000 training images and 10000 test images.
"""
_DATA_URLS = {
"10k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset10k.tgz",
"100k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset100k.tgz",
}
_NAMES = []
class Cartoonset(datasets.GeneratorBasedBuilder):
"""Cartoonset-10k Data Set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="10k",
version=datasets.Version("1.0.0", ""),
description="Loads the Cartoonset-10k Data Set",
),
datasets.BuilderConfig(
name="100k",
version=datasets.Version("1.0.0", ""),
description="Loads the Cartoonset-10k Data Set",
),
]
DEFAULT_CONFIG_NAME = "10k"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"img": datasets.Image(),
# "label": datasets.features.ClassLabel(names=_NAMES),
}
),
supervised_keys=("img",),
homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
citation=_CITATION,
# task_templates=ImageClassification(
# image_column="img", label_column="label"
# ),
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
url = _DATA_URLS[self.config.name]
print("URL:", url)
exit()
archive = dl_manager.download(url)
print(archive)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_archive(archive),
"split": "train",
},
),
# datasets.SplitGenerator(
# name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
# ),
]
def _generate_examples(self, files, split):
"""This function returns the examples in the raw (text) form."""
# if split == "train":
# batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
# if split == "test":
# batches = ["test_batch"]
# batches = [f"Cartoonset-10k-batches-py/{filename}" for filename in batches]
print("FILES", files)
path: str
for path, file_obj in files:
if path.endswith(".png"):
image = PIL.Image.open(path)
yield path, {
"img": np.asarray(image),
}
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